Bodge: Python package for efficient tight-binding modeling of superconductors.
Project description
Bodge
Bodge is a Python package for constructing large real-space tight-binding models. Although quite general tight-binding models can be constructed, we focus on the Bogoliubov-DeGennes ("BoDGe") Hamiltonian, which is used to model superconductivity in clean materials. In other words: If you want a lattice model for superconducting nanostructures, and want something that is computationally efficient yet easy to use, you've come to the right place. During my PostDoc, this code was used to investigate several interesting research topics in superconductivity, including the DC Josephson effect in altermagnets and RKKY interaction in triplet superconductors.
Internally, the package uses a sparse matrix (scipy.sparse) to represent the
Hamiltonian, which allows you to efficiently construct tight-binding models
with millions of lattice sites if needed. However, you can also easily convert
the result to a dense matrix (numpy.array) if that's more convenient. The
package follows modern software development practices: full test coverage
(pytest), runtime type checks (beartype), and PEP-8 compliance (black).
Quickstart
This package is published on PyPi, and is easily installed via pip:
pip install bodge
One of the main features of this package is a simple syntax if all you want to do is to create a real-space lattice Hamiltonian with some superconductivity. For instance, say that you want a $100\times100$ s-wave superconductor with a chemical potential $μ = -3t$, s-wave superconducting gap $Δ_s = 0.1t$, magnetic spin splitting along the z-direction $m = 0.05t$, and with nearest-neighbor hopping parameter $t = 1$. Using Bodge, you write:
from bodge import *
lattice = CubicLattice((100, 100, 1))
system = Hamiltonian(lattice)
t = 1
μ = -3 * t
m = 0.05 * t
Δs = 0.10 * t
with system as (H, Δ):
for i in lattice.sites():
H[i, i] = -μ * σ0 -m * σ3
Δ[i, i] = -Δs * jσ2
for i, j in lattice.bonds():
H[i, j] = -t * σ0
If you are familiar with tight-binding models, you might notice that this is
intentionally very close to how one might write the corresponding equations
with pen and paper. It is similarly easy to model more complex systems: you can
e.g. easily add p-wave or d-wave superconductivity, magnetism, altermagnetism,
antiferromagnetism, spin-orbit coupling, or inhomogeneities like interfaces. In
general, H[i, i] and H[i, j] set the on-site and nearest-neighbor "normal"
terms in the Hamiltonian matrix, and Δ[i, i] and Δ[i, j] set the
corresponding pairing terms representing superconductivity.
The syntax used to construct the Hamiltonian is designed to look like array
operations, but this is just a friendly interface; under the hood a lot of
"magic" occurs to efficiently translate what you see into sparse matrix
operations, while enforcing particle-hole and Hermitian symmetries. Once you're
done with the construction, you can call system.matrix() to extract the
Hamiltonian matrix itself (in dense or sparse form), or use methods such as
system.diagonalize() and system.free_energy() to get derived properties.
Development
After cloning the Git repository on a Unix-like system, you can run:
make install
This will create a virtual environment in a subfolder called venv,
and then install Bodge into that virtual environment. If you prefer to
use a newer Python version (recommended), first install this via your
package manager of choice. For example:
brew install python@3.11 # MacOS with HomeBrew
sudo apt install python3.11-full # Ubuntu GNU/Linux
Afterwards, mention what Python version to use when installing Bodge:
make install PYTHON=python3.11
Run make without any command-line arguments to see how to proceed
further. This should provide information on how to run the bundled
unit tests, run scripts that use the Bodge package, or run the
autoformatter after you have updated the code. PRs are welcome!
Acknowledgements
I wrote most of this code as a PostDoc in the research group of Prof. Jacob Linder at the Center for Quantum Spintronics, NTNU, Norway. I would like to thank Jacob for introducing me to the BdG formalism that is implemented in this package – and before that, to the theory superconductivity in general.
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